modify comment

example/yolov3_coco2017/train.py

@@ -67,7 +67,7 @@ if __name__ == '__main__':
     parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
-    parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or non-sink mode, default is sink")
+    parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink")
     parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
     parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
     parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")

mindspore/ccsrc/pipeline/pipeline_ge.cc

@@ -453,7 +453,7 @@ void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr>& info, const py::
   }
 
   // process the first args of tensor
-  // only in Dataset non-sink Mode, fp_bp graph need input tensors
+  // only in dataset normal(non-sink) mode, fp_bp graph need input tensors
   if (ConfigManager::GetInstance().dataset_mode() == DS_NORMAL_MODE) {
     for (std::size_t i = 0; i < size; i++) {
       ValuePtr converted = nullptr;

mindspore/ccsrc/transform/convert.cc

@@ -447,7 +447,7 @@ void DfGraphConvertor::InitLoopVar(std::vector<ge::Operator> *init_input) {
     if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
       value = ConfigManager::GetInstance().iter_num();
     } else {
-      MS_LOG(INFO) << "Run with non-sink mode, the iterator number will always be 1";
+      MS_LOG(INFO) << "Run with normal(non-sink) mode, the iterator number will always be 1";
       value = 1;
       ConfigManager::GetInstance().set_iter_num(value);
     }

mindspore/nn/wrap/loss_scale.py

@@ -51,7 +51,7 @@ class DynamicLossScaleUpdateCell(Cell):
     In every training step, the loss scaling value  will be updated by loss scaling value/`scale_factor`
     when there is overflow. And it will be increased by loss scaling value * `scale_factor` if there is no
     overflow for a continuous `scale_window` steps. This cell is used for Graph mode training in which all
-    logic will be executed on device side(Another training mode is non-sink mode in which some logic will be
+    logic will be executed on device side(Another training mode is normal(non-sink) mode in which some logic will be
     executed on host).
 
     Args:

tests/ut/python/utils/test_callback.py

@@ -112,8 +112,8 @@ def test_save_checkpoint():
         os.remove('./test_files/test_ckpt-model.pkl')
 
 
-def test_loss_monitor_sink_model():
-    """Test loss monitor sink model."""
+def test_loss_monitor_sink_mode():
+    """Test loss monitor sink mode."""
     cb_params = _InternalCallbackParam()
     cb_params.cur_epoch_num = 4
     cb_params.cur_step_num = 2
@@ -131,8 +131,8 @@ def test_loss_monitor_sink_model():
     callbacklist.end(run_context)
 
 
-def test_loss_monitor_feed_model():
-    """Test loss monitor non-sink mode."""
+def test_loss_monitor_normal_mode():
+    """Test loss monitor normal(non-sink) mode."""
     cb_params = _InternalCallbackParam()
     run_context = RunContext(cb_params)
     loss_cb = LossMonitor(1)